Machine Learning-Based Prognostics for Central Heating and Cooling Plant Equipment Health Monitoring

Abstract

Fault detection, diagnostics, and prognostics (FDD&P) ensure the operation efficiency and safety of engineering systems. In the building domain, they can help significantly reduce energy consumption and improve occupant comfort. Specifically, prognostics are becoming increasingly important as a pro-active fault prevention strategy through continuously monitoring the health of energy systems. In this article, we develop a machine learning-based method for building systems. The proposed method can help develop predictive models from historical operation and maintenance data. After the detailed description of the proposed machine learning-based prognostic method, a case study involving prognostics on central heating and cooling plant (CHCP) equipment is provided. To this end, a year's worth of sensor and actuator data from four boilers and five chillers of a CHCP in Ottawa, Canada are collected. The plant operators are interviewed to understand how they handle failure events, and their logbooks are reviewed to extract the date and time of the recorded failure events. The sensor and actuator data up to two weeks prior to each of these failure events are used to develop regression tree models that predict time to failure (TTF). The results indicate that about half of the modeled failure events could be accurately predicted by looking at the data available in the distributed control system. Finally, the future work is outlined.